Monocle per depot
3 feature selection methods: 1) Same feature selection strategy as for the whole dataset (split each depot into T1T2T3 and T4T5, cluster, perform DE tests on clusters, take union of resulting gene lists as features); 2) Genes with high dispersion; 3) Genes used for computing the trajectory of the whole dataset.
cds_peri <- readRDS('output/monocle/180831/peri/monocle_peri_T1T2T3_T4T5_res1/10x-180831-peri-monocle')
cds_peri_disp <- readRDS('output/monocle/180831/peri/monocle_peri_high-dispersion/10x-180831-peri-monocle')
cds_peri_gl <- readRDS('output/monocle/180831/peri/monocle_peri_genelist/10x-180831-peri-monocle')
plot_grid(
plot_cell_trajectory(cds_peri, color_by='timepoint'),
plot_cell_trajectory(cds_peri_disp, color_by='timepoint'),
plot_cell_trajectory(cds_peri_gl, color_by='timepoint'),
plot_cell_trajectory(cds_peri, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_peri_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_peri_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
ncol=3
)
cds_supra <- readRDS('output/monocle/180831/supra/monocle_supra_T1T2T3_T4T5_res1/10x-180831-supra-monocle')
cds_supra_disp <- readRDS('output/monocle/180831/supra/monocle_supra_high-dispersion/10x-180831-supra-monocle')
cds_supra_gl <- readRDS('output/monocle/180831/supra/monocle_supra_genelist/10x-180831-supra-monocle')
plot_grid(
plot_cell_trajectory(cds_supra, color_by='timepoint'),
plot_cell_trajectory(cds_supra_disp, color_by='timepoint'),
plot_cell_trajectory(cds_supra_gl, color_by='timepoint'),
plot_cell_trajectory(cds_supra, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_supra_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_supra_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
ncol=3
)
cds_subq <- readRDS('output/monocle/180831/subq/monocle_subq_T1T2T3_T4T5_res1/10x-180831-subq-monocle')
cds_subq_disp <- readRDS('output/monocle/180831/subq/monocle_subq_high-dispersion/10x-180831-subq-monocle')
cds_subq_gl <- readRDS('output/monocle/180831/subq/monocle_subq_genelist/10x-180831-subq-monocle')
plot_grid(
plot_cell_trajectory(cds_subq, color_by='timepoint'),
plot_cell_trajectory(cds_subq_disp, color_by='timepoint'),
plot_cell_trajectory(cds_subq_gl, color_by='timepoint'),
plot_cell_trajectory(cds_subq, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_subq_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_subq_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
ncol=3
)
cds_visce <- readRDS('output/monocle/180831/visce/monocle_visce_T1T2T3_T4T5_res1/10x-180831-visce-monocle')
cds_visce_disp <- readRDS('output/monocle/180831/visce/monocle_visce_high-dispersion/10x-180831-visce-monocle')
cds_visce_gl <- readRDS('output/monocle/180831/visce/monocle_visce_genelist/10x-180831-visce-monocle')
plot_grid(
plot_cell_trajectory(cds_visce, color_by='timepoint'),
plot_cell_trajectory(cds_visce_disp, color_by='timepoint'),
plot_cell_trajectory(cds_visce_gl, color_by='timepoint'),
plot_cell_trajectory(cds_visce, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_visce_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_visce_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
ncol=3
)
plot_grid(
plot_cell_trajectory(cds_peri, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_supra, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_subq, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
plot_cell_trajectory(cds_visce, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
ncol=2
)
plot_grid(
plot_cell_trajectory(cds_peri, color_by='State'),
plot_cell_trajectory(cds_supra, color_by='State'),
plot_cell_trajectory(cds_subq, color_by='State'),
plot_cell_trajectory(cds_visce, color_by='State'),
ncol=2
)
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